similar to: stats::reshape quadratic in number of input columns

On 2/20/19 7:55 PM, Toby Hocking wrote: > Update: I have observed that stringi::stri_sub is linear time complexity, > and it computes the same thing as base::substring. figure > https://github.com/tdhock/namedCapture-article/blob/master/figure-substring-bug.png > source: > https://github.com/tdhock/namedCapture-article/blob/master/figure-substring-bug.R > > To me this is a

Update: I have observed that stringi::stri_sub is linear time complexity, and it computes the same thing as base::substring. figure https://github.com/tdhock/namedCapture-article/blob/master/figure-substring-bug.png source: https://github.com/tdhock/namedCapture-article/blob/master/figure-substring-bug.R To me this is a clear indication of a bug in substring, but again it would be nice to have

Hi all, (and especially hi to Tomas Kalibera who accepted my patch sent yesterday) I believe that I have found another bug, this time in the substring function. The use case that I am concerned with is when there is a single (character scalar) text/subject, and many substrings to extract. For example substring("AAAA", 1:4, 1:4) or more generally, N=1000

Dear R-devel, A number of people have observed anecdotally that read.csv is slow for large number of columns, for example: https://stackoverflow.com/questions/7327851/read-csv-is-extremely-slow-in-reading-csv-files-with-large-numbers-of-columns I did a systematic comparison of read.csv with similar functions, and observed that read.csv is quadratic time (N^2) in the number of columns N, whereas

Dear R-devel, I did a systematic comparison of write.csv with similar functions, and observed two asymptotic inefficiencies that could be improved. 1. write.csv is quadratic time (N^2) in the number of columns N. Can write.csv be improved to use a linear time algorithm, so it can handle CSV files with larger numbers of columns? For more details including figures and session info, please see

Hi all, Several people have noticed that gregexpr is very slow for large subject strings when perl=TRUE is specified. - https://stackoverflow.com/questions/31216299/r-faster-gregexpr-for-very-large-strings - http://r.789695.n4.nabble.com/strsplit-perl-TRUE-gregexpr-perl-TRUE-very-slow-for-long-strings-td4727902.html - https://stat.ethz.ch/pipermail/r-help/2008-October/178451.html I figured out

Well it's only a leak if you don't get the memory back after it returns, right? Anyway, one (untested by me) possibility is the copying of memory pages when the garbage collector touches objects, as pointed out by Radford Neal here: http://r.789695.n4.nabble.com/Re-R-devel-Digest-Vol-149-Issue-22-td4710367.html If so, I don't think this would be easily avoidable, but there may be

It is known, with a known workaround, see e.g. https://www.stats.ox.ac.uk/pub/bdr/memtests/README.txt . Set suppressions in ~/.valgrindrc, e.g. the CRAN check machine has --suppressions=/data/blackswan/ripley/wcsrtombs.supp It is an issue in your OS (glibc), not TRE nor R. On 10/06/2020 00:21, Toby Hocking wrote: > Hi all, > > I'm on Ubuntu 18.04, running R-4.0.0 which I

Toby, > On Sep 2, 2015, at 1:12 PM, Toby Hocking <tdhock5 at gmail.com> wrote: > > Dear R-devel, > > I am running mclapply with many iterations over a function that modifies > nothing and makes no copies of anything. It is taking up a lot of memory, > so it seems to me like this is a bug. Should I post this to > bugs.r-project.org? > > A minimal reproducible

Dear R-devel, I am running mclapply with many iterations over a function that modifies nothing and makes no copies of anything. It is taking up a lot of memory, so it seems to me like this is a bug. Should I post this to bugs.r-project.org? A minimal reproducible example can be obtained by first starting a memory monitoring program such as htop, and then executing the following code while

Hello, I've been using R for about 3 years now and I'm pretty sure this is a bug. I'm using R 2.2.0. The way R is set up to get packages from CRAN using install.packages is really convenient --- if you are installing to your system's main package directory. However, I observe the following problem: I want package X but it requires package Y. Further, I have neither package

Hi all, I'm on Ubuntu 18.04, running R-4.0.0 which I compiled from source, and using valgrind I am always seeing the following message. Does anybody else see that? Is that a known false positive? Any ideas how to fix/suppress? Seems related to TRE, do I need to upgrade that? (base) tdhock at maude-MacBookPro:~/R/binsegRcpp$ R --vanilla -d valgrind -e 'extSoftVersion()' ==9565==

Hi Hilmar and Ivan, I have used your code examples to write a blog post about this topic, which has figures that show the asymptotic time complexity of the various approaches, https://tdhock.github.io/blog/2023/df-partial-match/ The asymptotic complexity of partial matching appears to be quadratic O(N^2) whereas the other approaches are asymptotically faster: linear O(N) or log-linear O(N log N).

Hi, I'm trying to reshape my data set from wide to tall format for multilevel modeling. Unfortunately, the function I typically use (make.univ from the multilevel package) does not appear to work with unbalanced data frames, which is what I'm dealing with. Below is an example of the columns of a data frame similar to what I'm working with: ID a1 a2 a4 b2 b3 b4 b5 b6 Below

Hello R users and developers, I have a general question about reshaping a wide data frame using the "reshape" command. I have a data frame consisting of 108 columns that I would like to convert to a long table and remove the metadata (embedded in the column names of the wide table) to new metadata columns in the long format. #example data frame. NOTE column names contain metadata::

Hi Toby, AFAIK it has not been addressed in R. You can handle the problem on your package side, see https://github.com/Rdatatable/data.table/pull/3237 Regards, Jan On Thu, May 30, 2019 at 4:46 AM Toby Hocking <tdhock5 at gmail.com> wrote: > > Hi all, > > I am having an issue related to installing packages on windows with > R-3.6.0. When installing a package that is in use, I

How do I calculate the confidence interval for the value x given by the intersection of two quadratics (i.e. parabolas)? I fit two quadratics of the form: y = C1 + B1*x + A1*x^2 y = C2 + B2*x + A2*x^2 to two sets of points N1 and N2. I test for whether they intersect, if they do then I calculate the roots of: 0 = (C1 - C2) + (B1 - B2)*x + (A1 - A2)*x^2 to determine where they intersect

-----Original Message----- Message: 6 Date: Mon, 20 Jan 2003 01:11:24 +0100 From: Martin Michlmayr <tbm at cyrius.com> To: r-help at stat.math.ethz.ch Subject: [R] quadratic trends and changes in slopes I'd like to use linear and quadratic trend analysis in order to find out a change in slope. Basically, I need to solve a similar problem as discussed in

Hi, Does anybody have experience to solve an quadratic programming problem with quadratic constraints in R? It seems that the package "quadprog" only handles the quadratic programming with linear constraint. My probelm is to maximze x^T\Sigma_{xy} y, subject to x^Tx=1, y^T\Sigma_{yy} y=1, and sum(y)<t, or sum(y)=t, where x and y are the variable, and the Sigma's and t are

HI All, I am unable to solve a optimization Problem Please Help Me out of this to solve. The Optimization problem is as follows :- My objective function is linear and one of the constraint is quadratic. Min z = 5 * X1 + 9* X2 + 7.15 *X3 + 2 * X4 subject to X1 + X2 + X3 +X4 = 9 X1 + X4 < = 6.55 X3(X3 - 3.5) >=0 X1,X2,X3,X4 >=0 Now the problem is how to solve this kind of